Bayesian estimation for the Pareto income distribution

The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function. This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized for the doubly truncated gamma prior distribution. Now deceased.     

Estimation in an empirical bayes model for longitudinal and cross‐sectionally clustered binary data

Some studies generate data that can be grouped into clusters in more than one way. Consider for instance a smoking prevention study in which responses on smoking status are collected over several years in a cohort of students from a number of different schools. This yields longitudinal data, also cross‐sectionaliy clustered in schools. The authors present a model for analyzing binary data of this type, combining generalized estimating equations and estimation of random effects to address the longitudinal and cross‐sectional dependence, respectively. The estimation procedure for this model is discussed, as are the results of a simulation study used to investigate the properties of its estimates. An illustration using data from a smoking prevention trial is given.     

Trade options for a small open economy: The different impact of Taiwan exports to China and to other countries

This study aims to explore advantageous trade arrangements for a small open economy. To discover the arrangement, the fundamental question to ask is how trade affects a small economy. Due to the differential factor mobility, the two main streams of trade theory—new economic geography and comparative advantage—make divergent predictions of trade effects. This study identifies the differences in factor mobility and distinguishes the differential impacts of exports to China and other countries on Taiwan’s manufacturing clusters. Using Taiwan’s 2006 and 2011 census data and trade statistics, this study applies the two-stage least squares method to test the differential impacts. The findings reveal that the growth in Taiwan’s exports to other countries significantly increased the employment level of manufacturing clusters in Taiwan. However, such effects have not been found for exports to China. The lack of response in Taiwan’s local employment to exports to China can be inferred as a short-term exhibition of the long-run core-periphery effect. The policy implication of this study is that trade involving low factor mobility is more beneficial than that involving high factor mobility for a small open economy. Thus, for a small economy, trade liberalization that will ‘not’ attract large factor outflows from the small economy is more desirable.     

“江蚁”抗逆力的实证研究

“蚁族”是对高校毕业生低收入聚居群体的典型概括,被视为继农民、农民工、下岗工人之后的第四大弱势群体.本文采用实证主义研究方法,运用自编量表对“江蚁”的抗逆力水平进行测量与分析,得出结论:“江蚁”普遍面临着生存压力,其中三成缺乏必要的抗逆力;外在保护因素严重不足,自我效能感(自信心)与自知力(自我认同度)偏低是影响抗逆力...     

Attitudes to debt among indebted undergraduates: A cross-national exploratory factor analysis

This paper reports the results of a cross-national study spanning England, New Zealand and the United States. A total of 496 first year undergraduates studying business or social science completed a 20-item questionnaire. This focused on their attitudes to their debt incurred while studying, as measured on a five-point Likert scale. A factor analysis model was developed, from which four consistent factors emerged, explaining 45 percent of the variation and consistent between countries. These factors were named: Anxiety, Utility-For-Lifestyle, Utility-For-Investment and Awareness. The first three factors were found to be uncorrelated with each other, but higher Awareness was associated with lower levels of Anxiety and Utility-For-Lifestyle and higher levels of Utility-For-Investment. The relationship with previous studies and implications for theory and practice are discussed.     

Bayes shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

The primary objective of a multi-regional clinical trial is to investigate the overall efficacy of the drug across regions and evaluate the possibility of applying the overall trial result to some specific region. A challenge arises when there is not enough regional sample size. We focus on the problem of evaluating applicability of a drug to a specific region of interest under the criterion of preserving a certain proportion of the overall treatment effect in the region. We propose a variant of James-Stein shrinkage estimator in the empirical Bayes context for the region-specific treatment effect. The estimator has the features of accommodating the between-region variation and finiteness correction of bias. We also propose a truncated version of the proposed shrinkage estimator to further protect risk in the presence of extreme value of regional treatment effect. Based on the proposed estimator, we provide the consistency assessment criterion and sample size calculation for the region of interest. Simulations are conducted to demonstrate the performance of the proposed estimators in comparison with some existing methods. A hypothetical example is presented to illustrate the application of the proposed method.     

Measuring conflict management,emotional self-efficacy,and problem solving confidence in an evaluation of outdoor programs for inner-city youth in Baltimore,Maryland

Substantial evidence supports the value of outdoor education programs for promoting healthy adolescent development, yet measurement of program outcomes often lacks rigor. Accurately assessing the impacts of programs that seek to promote positive youth development is critical for determining whether youth are benefitting as intended, identifying best practices and areas for improvement, and informing decisions about which programs to invest in.We generated brief, customized instruments for measuring three outcomes among youth participants in Baltimore City Outward Bound programs: conflict management, emotional self-efficacy, and problem solving confidence. Measures were validated through exploratory and confirmatory factor analyses of pilot-testing data from two groups of program participants. We describe our process of identifying outcomes for measurement, developing and adapting measurement instruments, and validating these instruments.The finalized measures support evaluations of outdoor education programs serving urban adolescent youth. Such evaluations enhance accountability by determining if youth are benefiting from programs as intended, and strengthen the case for investment in programs with demonstrated success.     

A non-iterative posterior sampling algorithm for linear quantile regression model

In this article, a non-iterative posterior sampling algorithm for linear quantile regression model based on the asymmetric Laplace distribution is proposed. The algorithm combines the inverse Bayes formulae, sampling/importance resampling, and the expectation maximization algorithm to obtain independently and identically distributed samples approximately from the observed posterior distribution, which eliminates the convergence problems in the iterative Gibbs sampling and overcomes the difficulty in evaluating the standard deviance in the EM algorithm. The numeric results in simulations and application to the classical Engel data show that the non-iterative sampling algorithm is more effective than the Gibbs sampling and EM algorithm.     

Minimax prediction in finite populations

In this paper, minimax predictors are considered for some population quantities of interest, under some specific superpopulation models. Minimax predictors are derived under a binomial superpopulation model. The well known ratio estimator is shown to be minimax under the usual simple regression normal model. Nonparametric minimax predictors are considered under some models. Some of the predictors considered also shown to be admis s ible.     

Estimating with percentile grouped and truncated date from a scale parameter distribution

Data which is grouped and truncated is considered. We are given numbers n₁<…<n_k=n and we observe X_ni ),i=1,…k, and the tottal number of observations available (N> n_k is unknown. If the underlying distribution has one unknown parameter θ which enters as a scale parameter, we examine the form of the equations for both conditional, unconditional and modified maximum likelihood estimators of θ and N and examine when these estimators will be finite, and unique. We also develop expressions for asymptotic bias and search for modified estimators which minimize the maximum asymptotic bias. These results are specialized tG the zxponential distribution. Methods of computing the solutions to the likelihood equatims are also discussed.